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This approach involves using the SQL EXISTS clause to handle the duplicates. By defining a subquery that checks for existence of another row with the same email but a smaller id, we can delete the current row if such a row exists.
Time Complexity: O(n^2) due to the nested subquery, where n is the number of rows.
Space Complexity: O(1) as we are performing operations in place.
1DELETE p1 FROM Person p1 WHERE EXISTS (SELECT 1 FROM Person p2 WHERE p1.email = p2.email AND p1.id > p2.id);This SQL query deletes rows from the Person table where there exists another row within the same table that has the same email but a smaller id. Thus, it ensures that only one unique email with the smallest id is retained.
This approach uses a self-join to create joins within the same table. We join the table on the email field itself and use the condition to keep the entry with the smallest id.
Time Complexity: O(n) primarily, because the operation groups by email and selects min in linear time, although the exact operations may vary by SQL engine.
Space Complexity: O(n) due to the creation of the temporary table holding min ids.
1DELETE FROM Person WHERE id NOT IN (SELECT min_id FROM (SELECT MIN(id) This approach involves using a hash table (or dictionary) to store the smallest ID for each unique email. As you iterate over each email entry, check if the email already exists in the hash table. If it does, compare IDs and update the hash table with the smaller ID if necessary. After processing all entries, use the hash table data to filter out duplicates by retaining entries with only the smallest IDs.
Time Complexity: O(n log n) due to the sort operation.
Space Complexity: O(1) as sorting is done in place.
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This approach is specifically designed for SQL contexts. The strategy here is to delete entries from a database such that for each email, only the entry with the smallest ID is retained. This involves using SQL commands to isolate entries that have duplicate emails (but higher IDs than the minimum for each specific email) and then deleting them.
Time Complexity: Generally O(n log n) for the grouping operation.
Space Complexity: O(n) with respect to holding email and associated data in memory during sub-query execution.
1DELETE FROM Person
2WHERE id NOT IN (
3 SELECTThe strategy involves creating a subquery that selects the minimum id for each email group. We then delete any row from Person where its id is not in this set of minimum ids.
This Python solution uses a dictionary to map emails to their smallest IDs. As you iterate over person tuples, keep the lowest ID for each email in the dictionary. Finally, reconstruct the list of persons using the dictionary with unique emails and their smallest IDs.
This SQL solution uses a DELETE statement with a sub-query to identify entries that are not the minimum ID for a given email. The sub-query groups by email and selects the minimum ID for each group, effectively isolating the desired entries to be retained.